Classification Criteria for Spondyloarthritis/HLA-B27-Associated Anterior Uveitis

PURPOSE: The purpose of this study was to determine classification criteria for spondyloarthritis/HLA-B27-associated anterior uveitis DESIGN: Machine learning of cases with spondyloarthritis/HLA-B27-associated anterior uveitis and 8 other anterior uveitides. METHODS: Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,083 cases of anterior uveitides, including 184 cases of spondyloarthritis/HLA-B27-associated anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% CI: 92.4-98.6). Key criteria for spondyloarthritis/HLA-B27-associated anterior uveitis included 1) acute or recurrent acute unilateral or unilateral alternating anterior uveitis with either spondyloarthritis or a positive test result for HLA-B27; or 2) chronic anterior uveitis with a history of the classic course and either spondyloarthritis or HLA-B27; or 3) anterior uveitis with both spondyloarthritis and HLA-B27. The misclassification rates for spondyloarthritis/HLA-B27-associated anterior uveitis were 0% in the training set and 3.6% in the validation set. CONCLUSIONS: The criteria for spondyloarthritis/HLA-B27-associated anterior uveitis had a low misclassification rate and appeared to perform well enough for use in clinical and translational research. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study aimed to establish classification criteria for spondyloarthritis/HLA-B27-associated anterior uveitis using machine learning, achieving high accuracy in both training and validation sets. Key criteria for this condition were identified, with a low misclassification rate, indicating their potential utility in clinical and translational research.